论文标题
机器学习可以道德吗？
Can Machine Learning be Moral?
论文作者
论文摘要
机器学习的伦理已成为AI社区中不可避免的话题。机器学习系统在多种社会环境中的部署导致对这些系统的设计，开发和应用进行了更深入的道德审查。 AI/ML社区已经应考虑机器学习的道德含义的必要，不仅是产品，而且还作为实践（Birhane，2021； Shen等，2021）。令人困扰的许多辩论的关键问题是什么可以构成道德上负责的机器学习系统。在本文中，我们探讨了对机器学习方法的道德评估的可能性。我们从关系伦理学的角度审查机器学习中的技术，方法和技术实践，考虑到机器学习系统如何成为世界的一部分以及它们与不同形式的代理机构的关系。从Phil Chel（1997）中获取页面，我们将关键技术实践的概念作为对机器学习方法的分析手段。我们的激进建议是，监督的学习似乎是唯一可以在道德上辩护的机器学习方法。
The ethics of Machine Learning has become an unavoidable topic in the AI Community. The deployment of machine learning systems in multiple social contexts has resulted in a closer ethical scrutiny of the design, development, and application of these systems. The AI/ML community has come to terms with the imperative to think about the ethical implications of machine learning, not only as a product but also as a practice (Birhane, 2021; Shen et al. 2021). The critical question that is troubling many debates is what can constitute an ethically accountable machine learning system. In this paper we explore possibilities for ethical evaluation of machine learning methodologies. We scrutinize techniques, methods and technical practices in machine learning from a relational ethics perspective, taking into consideration how machine learning systems are part of the world and how they relate to different forms of agency. Taking a page from Phil Agre (1997) we use the notion of a critical technical practice as a means of analysis of machine learning approaches. Our radical proposal is that supervised learning appears to be the only machine learning method that is ethically defensible.
